Identification of a copper metabolism‐related gene signature for predicting prognosis and immune response in glioma

Abstract

Abstract Background Gliomas are highly refractory intracranial cancers characterized by genetic and transcriptional heterogeneity. However, therapeutic options are limited. In the last years, copper‐induced cell death is becoming a prospective treatment strategy for gliomas and other solid tumors, but copper metabolism‐related genes associated with cancer development remain unclear. Methods We first collected gene expression data from The Cancer Genome Atlas (TCGA) to identify significantly differentially expressed copper metabolism‐related genes in gliomas. Using these genes, we performed COX regression and Least Absolute Shrinkage and Selection Operator (LASSO) regression to construct the prognostic model. The prognostic value of the model was further validated by CGGA testing set. Subsequently, functional analyses were carried out, including gene set enrichment analysis (GSEA), immune infiltration analysis, and mutation analysis. Finally, the expression levels of these genes were verified by immunohistochemical analysis. Results The prognostic model consisted of 7 genes: CDK1, LOXL2, LOXL3, NFE2L2, SLC31A1, SUMF1 and FDX1. According to this prognosis model, glioma patients could be split into the high‐risk group or low‐risk group, and the low‐risk group showed significantly better prognostic survival (p < 0.001). Moreover, the high‐risk group had higher levels of immune cell infiltration, immune checkpoint genes expression, and higher tumor mutational burden (TMB), which indicates that they might benefit more from immunotherapy. Finally, we confirmed the expression level of FDX1, SUMF1, and SLC31A1 protein as significantly different in glioblastoma, lower‐grade glioma, and non‐tumor brain tissues by immunohistochemical analysis, and the high expression of FDX1 and SLC31A1 protein was related to poor survival in glioma patients. Conclusions Our study could contribute to the prognosis prediction and decision‐making in patients with gliomas.

Keywords

• bioinformatics
• cancer immunity
• copper metabolism
• diffuse glioma
• prognosis